DETAILED ACTION
Status of Application
This action is a Non-Final Rejection. This action is in response to the request for continued examination filed on October 21, 2025.
Claims 1, 10, and 16 have been amended.
Claims 1-20 are pending and rejected.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Response to Arguments
Regarding the rejection under 35 U.S.C. § 101, Applicant argues that “the present subject matter uses software to improve functionality of hardware, by improving upon the traditional manner in which a user’s preferences and investment activities, as well as an investment company’s values and activities, are identified and correlated.” Remarks at 6. However, Applicant has not shown that there is an improvement to any of the claimed technology.
Applicant further argues that the claimed “subject matter represents a practical application of any alleged abstract idea, by providing the ability to use artificial intelligence in a unique and nonobvious way to determine contradictions by the user and independently rate a portion of the user’s investment based on the user’s preference data and tracked investment activities.” Remarks at 7. However, Applicant is describing the abstract idea. The artificial intelligence is recited at a high level and is being used to implement the abstract idea.
Applicant further argues that “[t]he amendment to the independent claims overcomes the § 101 rejection by providing a technological improvement similar to that found patent-eligible in Enfish. Like the self-referential table in Enfish that improved computer database functionality, the claimed AI system that determines contradictions between stated company values and actual activities represents a specific technological solution that improves computer functionality in the field of investment analysis.” Remarks at 7. Applicant further asserts that “[t]he amendment describes a concrete technological process that uses artificial intelligence in a specific way to solve the technical problem of identifying companies whose publicly available ratings do not align with their actual activities – a problem that could not be solved by conventional computer systems without this specified AI functionality.” Id. In Enfish, “the features were not conventional and thus were considered to reflect an improvement to existing technology. In particular, they enabled the claimed table to achieve benefits over conventional databases, such as increased flexibility, faster search times, and smaller memory requirements.” MPEP 2106.05(d). A similar improvement to technology is not provided in the instant claims. Determining contradictions between stated company values and actual activities does not provide an improvement to technology or a technological field.
Applicant further argues that “[t]he amended claim integrates the abstract idea into a practical application by providing a concrete technological solution that improves computer functionality. This specific technical process involves the computer system using machine learning to determine contradictions between stated company values an actual company activities to identify companies with publicly available ratings that do not align with company activity.” Remarks at 7. Applicant is describing an alleged improvement to the abstract idea and not an improvement to computer functionality.
Applicant further argues that the instant claims are similar to those in McRO because “[t]he present claims describe a similar automation to actively analyze investment data and automatically generate corrected ratings based on detected contradictions between user values and actual investment characteristics.” Remarks at 8. However, the claims in McRO were directed to an improvement in computer animation and not to an abstract idea such as a business process. The claims in McRO also described a specific way (use of particular rules to set morph weights and transitions through phonemes) to solve the problem of producing accurate and realistic lip synchronization and facial expressions in animated characters, rather than merely claiming the idea of a solution or outcome, and thus were not directed to an abstract idea. See MPEP 2106.05(a)
Applicant further points to the statement in MPEP 2106 that “[t]he claim itself does not need to explicitly recite the improvement described in the specification.” Remarks at 9. However, although Applicant refers to paragraphs 0010, 0011, and 0013 of the Specification (see Remarks at 8-9), Applicant has not shown that these paragraphs described an improvement to technology or to a technological field. Although paragraph 0013 refers to a “specialized computer system,” it does not provide any details that suggest that the computer is anything other than a programmed general purpose computing device.
As such, the rejection under 35 U.S.C. § 101 is maintained.
Regarding the rejection under 35 U.S.C. § 103, the rejection is withdrawn in light of Applicant’s amendments. Although individual claim features are known in the art, claim 1 for example, as a whole, is not made obvious by the prior art.
Claim Rejections - 35 USC § 101
35 U.S.C. § 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Does the Claim Fall within a Statutory Category? (see MPEP 2106.03)
Yes, with respect to claims 1-9, which recite a method and, therefore, are directed to the statutory class of process.
Yes, with respect to claims 10-15, which recite a system and, therefore, are directed to the statutory class of machine or manufacture.
Yes, with respect to claims 16-20, which recite a non-transitory computer-readable storage medium and, therefore, are directed to the statutory class of manufacture.
Step 2A, Prong One: Is a Judicial Exception Recited? (see MPEP 2106.04(a))
The following claims identify the limitations that recite the abstract idea in regular text and that recite additional elements in bold:
1. A computer-implemented method comprising:
receiving, by a computer system, a user input indicating values or interests of a user;
analyzing, by the computer system, the user input to locate and extract preference data from the user input;
tracking, by the computer system, activities of the user;
determining, by the computer system using machine learning, differences between the preference data and the tracked activities to illustrate contradictions by the user;
composing, by the computer system using the machine learning, a recommendation for the user based on the determined differences, wherein composing the recommendation includes using the machine learning to determine contradictions between stated company values and actual company activities to identify companies with publicly available ratings that do not align with company activity, and automatically generating an independent rating for at least a portion of a user investment; and
displaying, on a graphical user interface in communication with the computer system, the recommendation to assist the user in aligning the activities of the user with the values or interests of the user.
2. The computer-implemented method of claim 1, wherein the user input is related to sustainability ratings of an exchange-traded fund (ETF).
3. The computer-implemented method of claim 1, wherein the activities of the user include investment activities of the user.
4. The computer-implemented method of claim 3, further comprising: automatically adjusting, by the computer system using the machine learning, a user investment portfolio based on the recommendation.
5. The computer-implemented method of claim 1, wherein displaying the recommendation includes displaying a list of differences between the preference data and the tracked activities to illustrate contradictions by the user.
6. The computer-implemented method of claim 1, wherein using the machine learning includes using a machine learning model including one or more of a long short-term memory (LSTM) network, bidirectional encoder representations from transformers (BERT), natural language processing (NLP), or an artificial intelligence (AI)-based knowledge tree.
7. The computer-implemented method of claim 1, wherein displaying the recommendation includes displaying an article recommendation for a news article related to the activities of the user.
8. The computer-implemented method of claim 7, wherein displaying the article recommendation includes displaying the news article.
9. The computer-implemented method of claim 8, wherein displaying the news article includes composing, by the computer system using the machine learning, the news article.
10. A system comprising:
a computing system comprising one or more processors and a data storage system in communication with the one or more processors, wherein the data storage system comprises instructions thereon that, when executed by the one or more processors, causes the one or more processors to:
receive a user input indicating values or interests of a user;
analyze the user input to locate and extract preference data from the user input;
track activities of the user;
determine, using machine learning, differences between the preference data and the tracked activities to illustrate contradictions by the user;
compose, using the machine learning, a recommendation for the user based on the determined differences, wherein composing the recommendation includes using the machine learning to determine contradictions between stated company values and actual company activities to identify companies with publicly available ratings that do not align with company activity, and automatically generating an independent rating for at least a portion of a user investment; and
display, on a graphical user interface, the recommendation to assist the user in aligning the activities of the user with the values or interests of the user.
11. The system of claim 10, wherein the machine learning includes a machine learning model including a neural network.
12. The system of claim 11, wherein the neural network includes a long short-term memory (LSTM) network.
13. The system of claim 10, wherein the machine learning includes bidirectional encoder representations from transformers (BERT).
14. The system of claim 10, wherein the machine learning includes natural language processing (NLP).
15. The system of claim 10, wherein the machine learning includes an artificial intelligence (AI)-based knowledge tree.
16. A non-transitory computer-readable storage medium, the non-transitory computer-readable storage medium including instructions that, when executed by computers, cause the computers to perform operations of:
receiving a user input indicating values or interests of a user;
analyzing the user input to locate and extract preference data from the user input;
tracking activities of the user;
determining, using machine learning, differences between the preference data and the tracked activities to illustrate contradictions by the user;
composing, using the machine learning, a recommendation for the user based on the determined differences, wherein composing the recommendation includes using the machine learning to determine contradictions between stated company values and actual company activities to identify companies with publicly available ratings that do not align with company activity, and automatically generating an independent rating for at least a portion of a user investment; and
displaying, on a graphical user interface, the recommendation to assist the user in aligning the activities of the user with the values or interests of the user.
17. The non-transitory computer-readable storage medium of claim 16, wherein the medium further includes instructions that, when executed by computers, cause the computers to perform operations of: providing an alert to the user based on the determined differences.
18. The non-transitory computer-readable storage medium of claim 16, wherein using machine learning includes using a machine learning model including one or more of a long short-term memory (LSTM) network, bidirectional encoder representations from transformers (BERT), natural language processing (NLP), or an artificial intelligence (AI)-based knowledge tree.
19. The non-transitory computer-readable storage medium of claim 16, wherein the activities of the user include investment activities of the user.
20. The non-transitory computer-readable storage medium of claim 19, wherein the medium further includes instructions that, when executed by computers, cause the computers to perform operations of: automatically adjusting, using the machine learning, a user investment portfolio based on the recommendation.
Yes. But for the recited additional elements as shown above in bold, the remaining limitations of the claims recite certain methods of organizing human activity. The claims are directed to recommending investments to a user that align with the user’s values. This type of method of organizing human activity is a fundamental economic practice because it involves investing and a commercial interaction such as agreements in the form of contracts, advertising, marketing or sales activities or behaviors, and business relations. Thus, the claims recite an abstract idea.
Step 2A, Prong Two: Is the Abstract Idea Integrated into a Practical Application? (see MPEP 2106.04(d))
No. The claims as a whole merely use a computer as a tool to perform the abstract idea. The computing components (i.e., additional elements that are in bold above) are recited at a high level of generality and are merely invoked as a tool to implement the steps. For example, only a programmed general purpose computing device is needed to implement the claimed process. Additionally, the use of machine learning is recited at a high level. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. Additionally, there is no improvement to the functioning of a computer or technology. Therefore, the abstract idea is not integrated into a practical application.
Step 2B: Does the Claim Provide an Inventive Concept? (see MPEP 2106.05)
No. As discussed with respect to Step 2A, Prong 2, the additional elements in the claims, both individually and in combination, amount to no more than tools to perform the abstract idea. Merely performing the abstract idea using a computer cannot provide an inventive concept. Therefore, the claims do not provide an inventive concept.
As such, the claims are not patent eligible.
Relevant Prior Art
The following references are relevant to Applicant’s invention:
Jain, U.S. Patent Application Publication Number 2023/0385934 A1. This reference teaches a method for providing financial recommendations for investors with preferences for non-financial characteristics.
Wirth et al., U.S. Patent Application Publication Number 2012/0016807 A1. This reference teaches a personalized financial illustration system.
Pathak et al., U.S. Patent Application Publication Number 2020/0160442 A1. This reference teaches generating company ratings based on sustainability.
Conclusion
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/ELIZABETH H ROSEN/Primary Examiner, 3693